{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6912ab05-f66a-40a9-a4a5-4deb80d2e0d9",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/langchain/langgraph_rag_agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79e9c850-d829-4266-a2b6-4e69ad24e30e",
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install -U langchain_groq langchain langgraph langchain_community sentence_transformers tavily-python tiktoken langchainhub chromadb"
   ]
  },
  {
   "attachments": {
    "dccfae03-f250-494e-82d6-f229eafb0ea6.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "783f3dba-888c-4b74-a29e-b5d7a2386712",
   "metadata": {},
   "source": [
    "# LangGraph RAG agent with Llama 3\n",
    "\n",
    "Previously, we showed how to build simple agents with LangGraph and Llama 3.\n",
    "\n",
    "Now, we'll pick a more advanced use-case: advanced RAG.\n",
    "\n",
    "## Ideas\n",
    "\n",
    "We'll combine ideas from three RAG papers into a RAG agent:\n",
    "\n",
    "- **Routing:**  Adaptive RAG ([paper](https://arxiv.org/abs/2403.14403)). Route questions to different retrieval approaches\n",
    "- **Fallback:** Corrective RAG ([paper](https://arxiv.org/pdf/2401.15884.pdf)). Fallback to web search if docs are not relevant to query\n",
    "- **Self-correction:** Self-RAG ([paper](https://arxiv.org/abs/2310.11511)). Fix answers w/ hallucinations or don’t address question\n",
    "\n",
    "![Screenshot 2024-05-03 at 10.50.02 AM.png](attachment:dccfae03-f250-494e-82d6-f229eafb0ea6.png)\n",
    "\n",
    "Note that this will incorperate [a few general ideas for agents](https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/):\n",
    "\n",
    "- **Reflection**: The self-correction mechanism is a form of reflection, where the LangGraph agent reflects on its retrieval and generations\n",
    "- **Planning**: The control flow laid out in the graph is a form of planning \n",
    "- **Tool use**: Specific nodes in the control flow (e.g., web search) will use tools\n",
    "\n",
    "## Models\n",
    "\n",
    "### LLM\n",
    "\n",
    "We can use one of the providers that (1) offer Llama 3 and (2) [provide structure outputs](https://python.langchain.com/docs/modules/model_io/chat/structured_output/).\n",
    "\n",
    "Here, we use [Groq](https://groq.com/).\n",
    "\n",
    "### Tracing\n",
    "\n",
    "```\n",
    "### Tracing (optional)\n",
    "os.environ['LANGCHAIN_TRACING_V2'] = 'true'\n",
    "os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'\n",
    "os.environ['LANGCHAIN_API_KEY'] = 'LANGCHAIN_API_KEY'\n",
    "```\n",
    "\n",
    "### Search\n",
    "\n",
    "Uses [Tavily](https://tavily.com/#api)m for web search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a688f62-a11e-4f44-8e66-0bf2a533dedf",
   "metadata": {},
   "outputs": [],
   "source": [
    "### LLMs\n",
    "import os\n",
    "\n",
    "os.environ['LANGCHAIN_TRACING_V2'] = 'true'\n",
    "os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'\n",
    "os.environ['LANGCHAIN_API_KEY'] = 'LANGCHAIN_API_KEY'\n",
    "\n",
    "os.environ['TAVILY_API_KEY'] = 'YOUR_TAVILY_API_KEY'\n",
    "os.environ['GROQ_API_KEY'] = 'YOUR_GROQ_API_KEY'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27a89322-0e88-4886-bcb4-3ac9bc1db316",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Build Index\n",
    "\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain_community.document_loaders import WebBaseLoader\n",
    "from langchain_community.vectorstores import Chroma\n",
    "from langchain_community.embeddings import HuggingFaceEmbeddings\n",
    "\n",
    "# Docs to index\n",
    "urls = [\n",
    "    \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n",
    "    \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n",
    "    \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n",
    "]\n",
    "\n",
    "# Load\n",
    "docs = [WebBaseLoader(url).load() for url in urls]\n",
    "docs_list = [item for sublist in docs for item in sublist]\n",
    "\n",
    "# Split\n",
    "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
    "    chunk_size=500, chunk_overlap=0\n",
    ")\n",
    "doc_splits = text_splitter.split_documents(docs_list)\n",
    "\n",
    "# Add to vectorstore\n",
    "vectorstore = Chroma.from_documents(\n",
    "    documents=doc_splits,\n",
    "    collection_name=\"rag-chroma\",\n",
    "    embedding=HuggingFaceEmbeddings(),\n",
    ")\n",
    "retriever = vectorstore.as_retriever()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1041afc4-6680-4be0-950a-1c5b80b6b895",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Router\n",
    "\n",
    "from typing import Literal\n",
    "\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
    "from langchain_groq import ChatGroq\n",
    "\n",
    "# Data model\n",
    "class RouteQuery(BaseModel):\n",
    "    \"\"\"Route a user query to the most relevant datasource.\"\"\"\n",
    "\n",
    "    datasource: Literal[\"vectorstore\", \"web_search\"] = Field(\n",
    "        ...,\n",
    "        description=\"Given a user question choose to route it to web search or a vectorstore.\",\n",
    "    )\n",
    "\n",
    "# LLM with function call \n",
    "llm = ChatGroq(temperature=0, model=\"llama3-70b-8192\")\n",
    "structured_llm_router = llm.with_structured_output(RouteQuery)\n",
    "\n",
    "# Prompt \n",
    "system = \"\"\"You are an expert at routing a user question to a vectorstore or web search.\n",
    "The vectorstore contains documents related to agents, prompt engineering, and adversarial attacks.\n",
    "Use the vectorstore for questions on these topics. Otherwise, use web-search.\"\"\"\n",
    "route_prompt = ChatPromptTemplate.from_messages(\n",
    "    [\n",
    "        (\"system\", system),\n",
    "        (\"human\", \"{question}\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "question_router = route_prompt | structured_llm_router\n",
    "print(question_router.invoke({\"question\": \"Who will the Bears draft first in the NFL draft?\"}))\n",
    "print(question_router.invoke({\"question\": \"What are the types of agent memory?\"}))### Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bd753fb-9330-4ffa-8721-f133dc8a86aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Retrieval Grader \n",
    "\n",
    "# Data model\n",
    "class GradeDocuments(BaseModel):\n",
    "    \"\"\"Binary score for relevance check on retrieved documents.\"\"\"\n",
    "\n",
    "    score: str = Field(description=\"Documents are relevant to the question, 'yes' or 'no'\")\n",
    "\n",
    "# LLM with function call \n",
    "llm = ChatGroq(temperature=0, model=\"llama3-70b-8192\")\n",
    "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
    "\n",
    "# Prompt \n",
    "system = \"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
    "    If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \\n\n",
    "    It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n",
    "    Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.\"\"\"\n",
    "grade_prompt = ChatPromptTemplate.from_messages(\n",
    "    [\n",
    "        (\"system\", system),\n",
    "        (\"human\", \"Retrieved document: \\n\\n {document} \\n\\n User question: {question}\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "retrieval_grader = grade_prompt | structured_llm_grader\n",
    "question = \"agent memory\"\n",
    "docs = retriever.get_relevant_documents(question)\n",
    "doc_txt = docs[1].page_content\n",
    "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d1fc9af-3426-491a-9d1c-3ccb3b7aba1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Generate\n",
    "\n",
    "from langchain import hub\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "\n",
    "# Prompt\n",
    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
    "\n",
    "# LLM\n",
    "llm = ChatGroq(temperature=0, model=\"llama3-70b-8192\")\n",
    "\n",
    "# Post-processing\n",
    "def format_docs(docs):\n",
    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
    "\n",
    "# Chain\n",
    "rag_chain = prompt | llm | StrOutputParser()\n",
    "\n",
    "# Run\n",
    "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
    "print(generation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0e522e7-4347-4b80-9972-8c9ed582995e",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Hallucination Grader \n",
    "\n",
    "# Data model\n",
    "class GradeHallucinations(BaseModel):\n",
    "    \"\"\"Binary score for hallucination present in generation answer.\"\"\"\n",
    "\n",
    "    score: str = Field(description=\"Answer is grounded in the facts, 'yes' or 'no'\")\n",
    "\n",
    "# LLM with function call \n",
    "llm = ChatGroq(temperature=0, model=\"llama3-70b-8192\")\n",
    "structured_llm_grader = llm.with_structured_output(GradeHallucinations)\n",
    "\n",
    "# Prompt \n",
    "system = \"\"\"You are a grader assessing whether an LLM generation is grounded in / supported by a set of retrieved facts. \\n \n",
    "     Give a binary score 'yes' or 'no'. 'Yes' means that the answer is grounded in / supported by the set of facts.\"\"\"\n",
    "hallucination_prompt = ChatPromptTemplate.from_messages(\n",
    "    [\n",
    "        (\"system\", system),\n",
    "        (\"human\", \"Set of facts: \\n\\n {documents} \\n\\n LLM generation: {generation}\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "hallucination_grader = hallucination_prompt | structured_llm_grader\n",
    "hallucination_grader.invoke({\"documents\": docs, \"generation\": generation})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daf65df4-72a2-4805-92a4-0025cb7db5ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Answer Grader \n",
    "\n",
    "# Data model\n",
    "class GradeAnswer(BaseModel):\n",
    "    \"\"\"Binary score to assess answer addresses question.\"\"\"\n",
    "\n",
    "    score: str = Field(description=\"Answer addresses the question, 'yes' or 'no'\")\n",
    "\n",
    "# LLM with function call \n",
    "llm = ChatGroq(temperature=0, model=\"llama3-70b-8192\")\n",
    "structured_llm_grader = llm.with_structured_output(GradeAnswer)\n",
    "\n",
    "# Prompt \n",
    "system = \"\"\"You are a grader assessing whether an answer addresses / resolves a question \\n \n",
    "     Give a binary score 'yes' or 'no'. Yes' means that the answer resolves the question.\"\"\"\n",
    "answer_prompt = ChatPromptTemplate.from_messages(\n",
    "    [\n",
    "        (\"system\", system),\n",
    "        (\"human\", \"User question: \\n\\n {question} \\n\\n LLM generation: {generation}\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "answer_grader = answer_prompt | structured_llm_grader\n",
    "answer_grader.invoke({\"question\": question,\"generation\": generation})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06a0af45-8b57-4ac6-a034-b368309f02cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Search\n",
    "\n",
    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
    "web_search_tool = TavilySearchResults(k=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b1c0474-4cd1-4802-b49b-dcbb70842411",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing_extensions import TypedDict\n",
    "from typing import List\n",
    "\n",
    "### State\n",
    "\n",
    "class GraphState(TypedDict):\n",
    "    \"\"\"\n",
    "    Represents the state of our graph.\n",
    "\n",
    "    Attributes:\n",
    "        question: question\n",
    "        generation: LLM generation\n",
    "        web_search: whether to add search\n",
    "        documents: list of documents \n",
    "    \"\"\"\n",
    "    question : str\n",
    "    generation : str\n",
    "    web_search : str\n",
    "    documents : List[str]\n",
    "\n",
    "from langchain.schema import Document\n",
    "\n",
    "### Nodes\n",
    "\n",
    "def retrieve(state):\n",
    "    \"\"\"\n",
    "    Retrieve documents from vectorstore\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        state (dict): New key added to state, documents, that contains retrieved documents\n",
    "    \"\"\"\n",
    "    print(\"---RETRIEVE---\")\n",
    "    question = state[\"question\"]\n",
    "\n",
    "    # Retrieval\n",
    "    documents = retriever.invoke(question)\n",
    "    return {\"documents\": documents, \"question\": question}\n",
    "\n",
    "def generate(state):\n",
    "    \"\"\"\n",
    "    Generate answer using RAG on retrieved documents\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        state (dict): New key added to state, generation, that contains LLM generation\n",
    "    \"\"\"\n",
    "    print(\"---GENERATE---\")\n",
    "    question = state[\"question\"]\n",
    "    documents = state[\"documents\"]\n",
    "    \n",
    "    # RAG generation\n",
    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
    "    return {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
    "\n",
    "def grade_documents(state):\n",
    "    \"\"\"\n",
    "    Determines whether the retrieved documents are relevant to the question\n",
    "    If any document is not relevant, we will set a flag to run web search\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        state (dict): Filtered out irrelevant documents and updated web_search state\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
    "    question = state[\"question\"]\n",
    "    documents = state[\"documents\"]\n",
    "    \n",
    "    # Score each doc\n",
    "    filtered_docs = []\n",
    "    web_search = \"No\"\n",
    "    for d in documents:\n",
    "        score = retrieval_grader.invoke({\"question\": question, \"document\": d.page_content})\n",
    "        grade = score.score\n",
    "        # Document relevant\n",
    "        if grade.lower() == \"yes\":\n",
    "            print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
    "            filtered_docs.append(d)\n",
    "        # Document not relevant\n",
    "        else:\n",
    "            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
    "            # We do not include the document in filtered_docs\n",
    "            # We set a flag to indicate that we want to run web search\n",
    "            web_search = \"Yes\"\n",
    "            continue\n",
    "    return {\"documents\": filtered_docs, \"question\": question, \"web_search\": web_search}\n",
    "    \n",
    "def web_search(state):\n",
    "    \"\"\"\n",
    "    Web search based based on the question\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        state (dict): Appended web results to documents\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---WEB SEARCH---\")\n",
    "    question = state[\"question\"]\n",
    "    documents = state[\"documents\"]\n",
    "\n",
    "    # Web search\n",
    "    docs = web_search_tool.invoke({\"query\": question})\n",
    "    web_results = \"\\n\".join([d[\"content\"] for d in docs])\n",
    "    web_results = Document(page_content=web_results)\n",
    "    if documents is not None:\n",
    "        documents.append(web_results)\n",
    "    else:\n",
    "        documents = [web_results]\n",
    "    return {\"documents\": documents, \"question\": question}\n",
    "\n",
    "### Conditional edge\n",
    "\n",
    "def route_question(state):\n",
    "    \"\"\"\n",
    "    Route question to web search or RAG.\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        str: Next node to call\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---ROUTE QUESTION---\")\n",
    "    question = state[\"question\"]\n",
    "    source = question_router.invoke({\"question\": question})  \n",
    "    if source.datasource == 'web_search':\n",
    "        print(\"---ROUTE QUESTION TO WEB SEARCH---\")\n",
    "        return \"websearch\"\n",
    "    elif source.datasource == 'vectorstore':\n",
    "        print(\"---ROUTE QUESTION TO RAG---\")\n",
    "        return \"vectorstore\"\n",
    "\n",
    "def decide_to_generate(state):\n",
    "    \"\"\"\n",
    "    Determines whether to generate an answer, or add web search\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        str: Binary decision for next node to call\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---ASSESS GRADED DOCUMENTS---\")\n",
    "    question = state[\"question\"]\n",
    "    web_search = state[\"web_search\"]\n",
    "    filtered_documents = state[\"documents\"]\n",
    "\n",
    "    if web_search == \"Yes\":\n",
    "        # All documents have been filtered check_relevance\n",
    "        # We will re-generate a new query\n",
    "        print(\"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, INCLUDE WEB SEARCH---\")\n",
    "        return \"websearch\"\n",
    "    else:\n",
    "        # We have relevant documents, so generate answer\n",
    "        print(\"---DECISION: GENERATE---\")\n",
    "        return \"generate\"\n",
    "\n",
    "### Conditional edge\n",
    "\n",
    "def grade_generation_v_documents_and_question(state):\n",
    "    \"\"\"\n",
    "    Determines whether the generation is grounded in the document and answers question.\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        str: Decision for next node to call\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---CHECK HALLUCINATIONS---\")\n",
    "    question = state[\"question\"]\n",
    "    documents = state[\"documents\"]\n",
    "    generation = state[\"generation\"]\n",
    "\n",
    "    score = hallucination_grader.invoke({\"documents\": documents, \"generation\": generation})\n",
    "    grade = score.score\n",
    "\n",
    "    # Check hallucination\n",
    "    if grade == \"yes\":\n",
    "        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n",
    "        # Check question-answering\n",
    "        print(\"---GRADE GENERATION vs QUESTION---\")\n",
    "        score = answer_grader.invoke({\"question\": question,\"generation\": generation})\n",
    "        grade = score.score\n",
    "        if grade == \"yes\":\n",
    "            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n",
    "            return \"useful\"\n",
    "        else:\n",
    "            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n",
    "            return \"not useful\"\n",
    "    else:\n",
    "        pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
    "        return \"not supported\"\n",
    "\n",
    "from langgraph.graph import END, StateGraph\n",
    "workflow = StateGraph(GraphState)\n",
    "\n",
    "# Define the nodes\n",
    "workflow.add_node(\"websearch\", web_search) # web search\n",
    "workflow.add_node(\"retrieve\", retrieve) # retrieve\n",
    "workflow.add_node(\"grade_documents\", grade_documents) # grade documents\n",
    "workflow.add_node(\"generate\", generate) # generatae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26a0ad7a-87d3-4e32-9608-ab9e4408ad76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build graph\n",
    "workflow.set_conditional_entry_point(\n",
    "    route_question,\n",
    "    {\n",
    "        \"websearch\": \"websearch\",\n",
    "        \"vectorstore\": \"retrieve\",\n",
    "    },\n",
    ")\n",
    "\n",
    "workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
    "workflow.add_conditional_edges(\n",
    "    \"grade_documents\",\n",
    "    decide_to_generate,\n",
    "    {\n",
    "        \"websearch\": \"websearch\",\n",
    "        \"generate\": \"generate\",\n",
    "    },\n",
    ")\n",
    "workflow.add_edge(\"websearch\", \"generate\")\n",
    "workflow.add_conditional_edges(\n",
    "    \"generate\",\n",
    "    grade_generation_v_documents_and_question,\n",
    "    {\n",
    "        \"not supported\": \"generate\",\n",
    "        \"useful\": END,\n",
    "        \"not useful\": \"websearch\",\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a2af1cb-4bba-4baf-9345-d21ce0e65503",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile\n",
    "app = workflow.compile()\n",
    "\n",
    "# Test\n",
    "from pprint import pprint\n",
    "inputs = {\"question\": \"What are the types of agent memory?\"}\n",
    "for output in app.stream(inputs):\n",
    "    for key, value in output.items():\n",
    "        pprint(f\"Finished running: {key}:\")\n",
    "pprint(value[\"generation\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4411adb6-a98d-41b4-ac00-5f643e008389",
   "metadata": {},
   "source": [
    "Trace: \n",
    "\n",
    "https://smith.langchain.com/public/2babc6ec-a243-40d0-844b-5e6b40f70fc9/r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0642aaaa-4657-45df-92f2-6a279d696497",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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